17 accepted papers
Dataset distillation has achieved remarkable progress as an effective approach for data compression. However, real-world data often comes from diverse domains, leading to potential mismatches between the domains of synthesized images and those of the evaluation set. Existing methods primarily assume
Decoding visual stimuli from electroencephalography (EEG) signals is a crucial step toward practical brain-computer interfaces (BCIs). However, this task requires large-scale and high-quality EEG-image paired datasets. Compared with abundant image data, the limited EEG recordings restrict the decodi
Cross-modal hashing (CMH) is an effective tool for large-scale retrieval due to its low storage cost and high efficiency. However, real-world multi-modal datasets often contain noisy annotations, which can significantly impair model performance. Many existing methods address this issue by using the
Cross-modal retrieval is crucial for discovering latent correspondences across different modalities. However, existing methods typically assume that training data are well-aligned, an unrealistic assumption since real-world datasets inevitably contain noisy correspondences. Many current approaches a
In-context learning (ICL), a paradigm derived from large language models (LLMs), holds significant promise but is notably sensitive to the choice of input demonstrations. While numerous methodologies have been developed to select the optimal demonstrations from existing datasets, our work alternativ
The sample selection approach is a widely adopted strategy for learning with noisy labels, where examples with lower losses are effectively treated as clean during training. However, this clean set often becomes dominated by easy examples, limiting the model's meaningful exposure to more challenging…
The surge in multi-modal data has propelled cross-modal matching to the forefront of research interest. However the challenge lies in the laborious and expensive process of curating a large and accurately matched multimodal dataset. Commonly sourced from the Internet these datasets often suffer from…
Exploring good generalization ability is essential in deep metric learning (DML). Most existing DML methods focus on improving the model robustness against category shift to keep the performance on unseen categories. However, in addition to category shift, domain shift also widely exists in real-wor…
In this paper, we empirically investigate a previously overlooked and widespread type of label noise, subclass-dominant label noise (SDN). Our findings reveal that, during the early stages of training, deep neural networks can rapidly memorize mislabeled examples in SDN. This phenomenon poses challe…
In label-noise learning, estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers. Traditionally, the transition from clean labels to noisy labels (i.e., clean-label transition matrix (CLTM)) has been widely exploited to l…
Similarity learning can be significantly advanced by informative relationships among different samples and features. The current methods try to excavate the multiple correlations in different aspects, but cannot integrate them into a unified framework. In this paper, we provide to consider the multi…
Deep cross-modal hashing has become an essential tool for supervised multimodal search. These models tend to be optimized with large, curated multimodal datasets, where most labels have been manually verified. Unfortunately, in many scenarios, such accurate labeling may not be available. In contrast…
Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augmentations have been introduced to further improve the diversity of training pairs. However, aggressive augmentations may…
By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning. However, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causi…
Multi-objective multi-task learning aims to boost the performance of all tasks by leveraging their correlation and conflict appropriately. Nevertheless, in real practice, users may have preference for certain tasks, and other tasks simply serve as privileged or auxiliary tasks to assist the training…
The memorization effect of deep neural network (DNN) plays a pivotal role in many state-of-the-art label-noise learning methods. To exploit this property, the early stopping trick, which stops the optimization at the early stage of training, is usually adopted. Current methods generally decide the…
Due to storage and search efficiency, hashing has become significantly prevalent for nearest neighbor search. Particularly, deep hashing methods have greatly improved the search performance, typically under supervised scenarios. In contrast, unsupervised deep hashing models can hardly achieve satis…